Comparison of proposed countermeasures for dilemma zone at signalized intersections based on cellular automata simulations

Wu, Yina; Abdel-Aty, Mohamed; Ding, YaoXian; Jia, Bin; Shi, Qi; Yan, Xuedong

doi:10.1016/j.aap.2017.09.009

Cited by 37 publications

(8 citation statements)

References 26 publications

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“…It is also recommended to include the identified geometric factors (e.g., bike lanes, sidewalk width) in simulation studies. Traffic simulation is a vital tool for improving roadway geometry and infrastructure ( 53 – 60 ). The simulation study could help further explain the reason that the identified factors have significant effects on the occurrence of bicycle crashes at intersections.…”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

Bicycle Safety Analysis at Intersections from Crowdsourced Data

Saad

Abdel-Aty

Lee

et al. 2019

Transportation Research Record

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Cycling is encouraged in countries around the world as an economic, energy efficient, and sustainable mode of transportation. Although there are many studies focusing on analyzing bicycle safety, they have limitations because of the shortage of bicycle exposure data. This study represents a major step forward in estimating safety performance functions for bicycle crashes at intersections by using crowdsourced data from STRAVA. Several adjustments in respect of the population distribution and field observations were made to overcome the disproportionate representation of the STRAVA data. The adjusted STRAVA data which include bicycle exposure information were used as input to develop safety performance functions. The functions are negative binomial models aimed at predicting frequencies of bicycle crashes at intersections. The developed model was compared with three counterparts: the model using the unadjusted STRAVA data, the model using the STRAVA data with field observation data adjustments only, and the model using the STRAVA data with adjusted population. The results revealed that the case of STRAVA data with both population and field observation data adjustments had the best performance in bicycle crash modeling. The results also addressed several key factors (e.g., signal control system, intersection size, bike lanes) which are associated with bicycle safety at intersections. Additionally, the safety-in-numbers effect was acknowledged when bicycle crash rates decreased as bicycle activities increased. The study concluded that crowdsourced data are a reliable source for exploring bicycle safety after the appropriate adjustments.

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Section: Conclusion and Recommendationsmentioning

confidence: 99%

Bicycle Safety Analysis at Intersections from Crowdsourced Data

Saad

Abdel-Aty

Lee

et al. 2019

Transportation Research Record

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“…Type I dilemma zone describes the region where drivers can neither stop safely nor pass through intersections. It has been postulated that this occurs due to insufficient yellow light time caused through high approaching speeds [15]. Type I dilemma zone is therefore formulated using vehicular information and intersection data, such as approach speed, acceleration, and yellow interval [4,16].…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Driver Decisions at the Onset of Yellow at Signalized Intersections

Juan

Jiang

Dong

et al. 2020

Journal of Advanced Transportation

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Drivers’ decisions to either slow and stop or go at the onset of yellow signal impact on intersection safety. This novel study contributes to the new classification scheme for drivers. Two driving style indexes (i.e., the driving reliability index and dangerous driving index) are adopted, along with other known factors to analyze stop/go decision-making. Initially, the driving reliability index is extracted using a Hidden Markov Model (HMM). The dangerous driving index is calculated based on statistics extracted from dangerous driving records. A latent class logit model is then proposed to explore the factors which influence drivers’ decisions. Drivers are classified for analytical purposes into “low-risk” and “high-risk” categories according to driving styles and age. Results indicate that those considering “low-risk” tend to stop, while drivers considering “high-risk” are inclined to pass intersections. Furthermore, distractions from cell phones have different influences on each group of drivers. These findings help to determine driver preferences and may be used to formulate strategies to reduce unsafe driving occurring at signalized intersections.

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“…In another road traffic-related application, Wu et al (see [29]) examine a number of warning systems aimed at helping drivers to decide on whether to stop or go when a green traffic light turns to red right in front of them. They construct a set of scenarios regarding combinations of warning measures and devise a CA-based model to evaluate them.…”

Section: Simulation Of Transportation Networkmentioning

confidence: 99%

Modeling and Simulation Using Cellular Automata

Ullrich¹,

Lückerath²

2020

SNE

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Cellular Automata are a simple, yet powerful modeling and simulation technique, easily employed if the system to be examined consists of a set of uniform entities that are located in a neighborhood relation to each other. The simple structure and fast execution of Cellular Automata allow for very large models, their inherent parallelism enables a comparatively simple parallelization. This paper gives an overview on the concepts of Cellular Automata-based modeling and simulation, with a special focus on neighhorhood concepts, border types, and transition functions, and describes current applications of the paradigm in the areas of the simulation of transportation networks, additive manufacturing, as well as urban growth and development. It is aimed at students of the craft as well as practitioners who might want to take a look beyond the GUI of their modeling tools.

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Comparison of proposed countermeasures for dilemma zone at signalized intersections based on cellular automata simulations

Cited by 37 publications

References 26 publications

Bicycle Safety Analysis at Intersections from Crowdsourced Data

Bicycle Safety Analysis at Intersections from Crowdsourced Data

Analysis of Driver Decisions at the Onset of Yellow at Signalized Intersections

Modeling and Simulation Using Cellular Automata

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